TraderNet-CR: Cryptocurrency Trading with Deep Reinforcement Learning - Artificial Intelligence Applications and Innovations Access content directly
Conference Papers Year : 2022

TraderNet-CR: Cryptocurrency Trading with Deep Reinforcement Learning

Abstract

The predominant method of developing trading strategies is technical analysis on historical market data. Other financial analysts monitor the public activity towards cryptocurrencies, in order to forecast upcoming trends in the market. Until now, the best cryptocurrency trading models rely solely on one of the two methodologies and attempt to maximize their profits, while disregarding the trading risk. In this paper, we present a new machine learning approach, named TraderNet-CR, which is based on deep reinforcement learning. TraderNet-CR combines both methodologies in order to detect profitable round trips in the cryptocurrency market and maximize a trader’s profits. Additionally, we have added an extension method, named N-Consecutive Actions, which examines the model’s previous actions, before suggesting a new action. This method is complementary to the model’s training and can be fruitfully combined, in order to further decrease the trading risk. Our experiments show that our model can properly forecast profitable round trips, despite high market commission fees.
Embargoed file
Embargoed file
0 7 19
Year Month Jours
Avant la publication
Wednesday, January 1, 2025
Embargoed file
Wednesday, January 1, 2025
Please log in to request access to the document

Dates and versions

hal-04317182 , version 1 (01-12-2023)

Licence

Attribution

Identifiers

Cite

Vasilis Kochliaridis, Eleftherios Kouloumpris, Ioannis Vlahavas. TraderNet-CR: Cryptocurrency Trading with Deep Reinforcement Learning. 18th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2022, Hersonissos, Greece. pp.304-315, ⟨10.1007/978-3-031-08333-4_25⟩. ⟨hal-04317182⟩
17 View
0 Download

Altmetric

Share

Gmail Facebook X LinkedIn More